Masked Autoencoder-Based Self-Supervised Learning for Electrocardiograms to Detect Left Ventricular Systolic Dysfunction.

Abstract

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. In this study, we aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of deep learning models that detect left ventricular systolic dysfunction (LVSD) from 12-lead electrocardiography data. In our MAE approach, we first mask the vast majority, that is, 75% of the ECG time series. Second, we pretrain a Vision Transformer encoder by inferring the masked part. Our proposed approach enables rich features that generalize well from unlabeled ECG data to be learned. In fact, the reconstructed ECG maintains the relationships among the major ECG components. Transfer performance in the detection of LVSD outperforms the baseline CNN model on external validation datasets and shows promising results for generalization that enables us to use the model for a broader population by solely using ECG data collected in a single medical institution.